DataRobot-Research/drdocs huggingface dataset:
---
title: API user guide
description: Review comprehensive workflows, notebooks, and tutorials that help you find complete examples of common data science and machine learning workflows.

---

# API user guide {: #api-user-guide }

The API user guide includes overviews, Jupyter notebooks, and task-based tutorials that help you find complete examples of common data science and machine learning workflows. Be sure to review the [API quickstart guide](api-quickstart/index) before using the notebooks below.

Topic | Describes... |
----- | ------ |
[Modeling workflow overview](modeling-workflow) | How to use DataRobot's clients, both Python and R, to train and experiment with models. |
[Python client v3.0](pythonv3) | Review the changes introduced to DataRobot's Python client with version 3.0. |
[Common use cases](common-case/index) | Review Jupyter notebooks that outline common use cases and machine learning workflows using DataRobot's Python client. |
[Public preview: R client v2.29](r-pub-prev/index) | Review the changes introduced to DataRobot's R client with version 2.29. |
[Python code examples](python/index) | Python code examples for common data science workflows. |
[R code examples](r-nb/index) |  code examples that outline common data science workflows. |
[REST API code examples](restapi/index) | REST API code examples that outline common data science workflows. |

In addition to the examples listed above, DataRobot hosts community-driven notebooks accessible from the following locations:

| Resource | Description   |
| --------------------------| ----------------------------- |
[Examples for data scientists (Github repository)](https://github.com/datarobot-community/examples-for-data-scientists/) | Referential Jupyter notebooks that outline common DataRobot functions.
[Tutorials for data scientists (Github repository)](https://github.com/datarobot-community/tutorials-for-data-scientists/) | Jupyter notebooks that detail applicable use cases for DataRobot.
[R vignettes included in the R client](https://cran.r-project.org/web/packages/datarobot/index.html) | Long-form tutorials outlining functions in the DataRobot R package.
[Jupyter notebooks included in the Python client](https://datarobot-public-api-client.readthedocs-hosted.com/) | Example Jupyter notebooks demonstrating sample use cases and DataRobot functions using the DataRobot Python package.
---
title: Platform home
description: Access the full DataRobot UI documentation, including feature descriptions for the UI and API, data preparation, tutorials, and a glossary.

---

# DataRobot UI documentation

The **UI docs** tab describes workflow and reference material for the UI version of the DataRobot AI Platform, regardless of deployment type.

Resource | Description
-------- | -----------
![](images/nc-tile-release.png)  | [**Get Started**](get-started/index): A quick introduction to analyzing data, creating models, and writing code with DataRobot.
![](images/nc-tile-workbench.png) | [**Workbench**](workbench/index): Workbench, an organizational hierarchy that creates a Use Case as a top level concept and supports experimentation and sharing.
![](images/nc-tile-data.png)  | [**Data**](data/index): Data management (import, transform, analyze, store) and the DataRobot Data Prep tool.
![](images/nc-tile-modeling.png)  | [**Modeling**](modeling/index): Building, understanding, and analyzing models; time series modeling; business operations tools; a modeling reference.
![](images/nc-tile-preds.png)  | [**Predictions**](predictions/index.html): The prediction API and associated reference; Scoring Code guide; batch scoring methods; UI-based prediction methods.
![](images/nc-tile-mlops.png)  | [**MLOps**](mlops/index): The pillars of the centralized hub for managing models in production&mdash; deployment, monitoring, managing, and governance.
![](images/nc-tile-code.png)  | [**Notebooks**](dr-notebooks/index): **Public Preview**: Create interactive, computational environments that hosts code execution and rich media for various use cases and workflows.
![](images/nc-tile-blueprint.png) | [**No-Code AI Apps**](app-builder/index): Use a no-code interface to configure AI-powered applications and enable core DataRobot services (making predictions, optimizing target features, simulating scenarios).
---
title: Scoring Code
description: How to export Scoring Code so that you can use DataRobot-generated models outside of the DataRobot platform.

---

# Scoring Code {: #scoring-code }

!!! info "Availability information"
     Contact your DataRobot representative for information on enabling the Scoring Code feature.

The **Scoring Code** feature exports Scoring Code for qualifying Leaderboard models, allowing you to use DataRobot-generated models outside the platform.

For more information, see the Scoring Code [considerations](#feature-considerations).

The following sections describe how to work with Scoring Code:

| Topic | Describes |
|-------|-----------|
| [Scoring Code overview](sc-overview) | Scoring Code, how you download it, and how to score with it. |
| [Download Scoring Code from the Leaderboard](sc-download-leaderboard) | Downloading and configuring Scoring Code from the Leaderboard. |
| [Download Scoring Code from a deployment](sc-download-deployment) | Downloading and configuring Scoring Code from a deployment. |
| [Download time series Scoring Code](sc-time-series) | Downloading and configuring Scoring Code for a time series project. |
| [Scoring at the command line](scoring-cli) | Syntax for scoring with embedded CLI. |
| [Scoring Code usage examples](quickstart-api) | Examples showing how to use the Scoring Code JAR to score from the CLI and in a Java project.   |
| [JAR structure](jar-package) | The contents of the Scoring Code JAR package. |
| [Generate Java models in an existing project](build-verify) | Retraining models that were created before the Scoring Code feature was enabled. |
| [Backward-compatible Java API](java-back-compat) |  Using Scoring Code with models created on different versions of DataRobot. |
| [Scoring Code JAR integrations](sc-jar-integrations) | Deploying DataRobot Scoring Code on an external platform. |

{% include 'includes/scoring-code-consider.md' %}
---
title: Troubleshooting 2FA
description: Help with two-factor authentication (2FA), an opt-in feature that provides additional security for DataRobot users.

---

#  Troubleshooting 2FA {: #troubleshooting-2fa }

Two-factor authentication (2FA) is an opt-in feature that provides additional security for DataRobot users. See the full documentation for instructions on [setting up 2FA](2fa); see the tips below for troubleshooting assistance.

## I am receiving a message that my code is invalid {: #i-am-receiving-a-message-that-my-code-is-invalid  }

* Make sure that you have only one instance of DataRobot authentication in your authenticator app. Each time you scan the QR code, the authenticator app creates a new account based on that code. The code you enter must be associated with the QR code displayed, and with multiple entries, it can be unclear which code to enter.

**Solution**: Rename or delete any DataRobot accounts listed in your authentication app.

* To do this with Google, for example, click the pencil icon and select all accounts registered to DataRobot. Select **DELETE** and when prompted, select **REMOVE ACCOUNTS**. (To reinstate the account, you can toggle "Enable two-factor authentication" in **Settings** and recapture the QR code).

## So many codes! {: #so-many-codes  }

* Some authentication systems (Google, for example) add new accounts to the bottom of the list.

**Solution**: When prompted for a code, enter the last DataRobot entry.

## I lost my codes {: #i-lost-my-codes  }

**Solution**: If you lose access to your phone and recovery codes, contact your administrator or DataRobot Support.

## I no longer want to use 2FA {: #i-no-longer-want-to-use-2fa  }

**Solution**: Toggle the feature off on the [**Settings**](user-settings) page.

![](images/2fa-7.png)

Enter a 6-digit authentication code or a saved recovery code and click **Disable**. The feature is removed from your account, but you can re-enable it at any time.

## I forgot my password (but I have my code) {: #i-forgot-my-password-but-i-have-my-code }

**Solution**: From the login page, click **Don't Remember?** and then on the next screen, click **Reset Password**:

![](images/2fa-8.png)

When prompted, enter your authentication app code or, if you don't have your mobile device, click **Switch to recovery code** and enter one of your saved codes.

DataRobot will send a link to reset your password.
# Version 8.0.10 {: #version-8010 }

_December 19, 2022_

The DataRobot v8.0.10 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v8.0.0 release notes for:

* [Features introduced in v8.0.0](v8.0.0-aml)

In addition to the issues listed below, DataRobot addressed several outstanding security issues. This release/update is part of DataRobot's continual effort to secure the product and platform.

## Issues fixed in v8.0.10 {: #issues-fixed-in-v8010 }

The following issues have been fixed since release v8.0.9:

### Enterprise {: #enterprise }

- EPS-691: Upgrades Spark from v3.0.1 to v.3.2.2. As a result, you must update BigQuery drivers using version 1.2.21.1025 or earlier and set up a new data connection with the **Google Bigquery - 2022** driver. Users who are using Hive on non-supported Hadoop versions will get an exception while uploading predictions from a Hive data source.
- EPS-688: Disables the host header validation by default. Documentation has been updated to explain how to enable host header validation by configuring the `ALLOWED_HOSTS` setting.

### Platform {: #platform }

- ARCH-4023: Updates the HTTP Strict Transport Security (HSTS) configuration settings to a value of 1 year.
- DM-8113: Fixes an issue causing Trino data connections to intermittently fail when performing a transaction.
- DM-7445: Upgrades the Python library `numpy` to 1.21.6. Users should not experience backward compatibility issues as a result of this change. The `numpy` library handles various numerical transformations related to data processing and preparation in the platform. DataRobot regularly upgrades libraries to improve speed, security, and predictive performance. Note that users who have trained and deployed a model using .xpt or .xport file formats may see predictions change (typically less than 1%). These changes are due to incremental differences in the treatment of floats between the current and target upgrade versions of the `numpy` library.
- PLT-7659: User roles now display in the **Roles** column on the **Users list** page.
- COPS-11659: Fixes an issue causing SELinux to deny `logrotate` access to files within the DataRobot `app_logs_dir` (e.g., `/opt/datarobot/logs`).

### Core modeling {: #core-modeling }

- MODEL-7313: The following fields are now configurable for Self-Managed AI Platform installations: `FROZEN_THRESHOLD`, `MAX_AUTOPILOT_SIZE`, `EDA_SAMPLE_THRESHOLD`, `CROSS_VALIDATION_THRESHOLD`, and `SLIM_RUN_THRESHOLD`.

### MLOps {: #mlops }

- MMM-9717: Fixes the ability for Self-Managed AI Platform users to compute insights for challenger model comparisons.

### Time series {: #time-series }

- TIME-12076: Fixes an issue causing prediction intervals differences in the UI and downloaded predictions for time series and multiseries projects.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Apply a DataRobot license
description: Learn how to apply a DataRobot license using either the user interface of DataRobot's API.
platform: self-managed-only

---

# Apply a DataRobot license {: #apply-a-datarobot-license }

This page explains how to apply a DataRobot license using either the application interface or the DataRobot API. Note that this workflow is exclusive to administrators, and requires you to be logged into DataRobot with admin permissions.

## Apply a license in the DataRobot application {: #apply-a-license-in-the-datarobot-application }

1. Access the DataRobot cluster in your browser.

2. Click the profile icon in the top-right corner of the page and select **License**.

    ![](images/license-1.png)

3. On the **License** page, enter the license key in the field and click **Validate**.

    ![](images/license-2.png)

4. After validating, DataRobot lists the features and functionality included in the subscription (blurred in the image below).

    ![](images/license-3.png)

5. Confirm that the validated license lists the correct subscription features, then click **Submit**.

## Apply a license via the API {: #apply-a-license-via-the-api }

Alternatively, administrators can validate a DataRobot license using DataRobot's REST API. To do so, provide your information in the snippet below and execute a REST API call.

``` shell
# Set the URI of the DataRobot App node
# Ex. https://datarobot.example.com
# Ex. http://10.2.3.4
dr_app_node="http://10.2.3.4"

# If you have a username and password, start here
# Set the initial username
admin_username=localadmin@datarobot.com

# Set the local administrator user password
admin_password=""

# Read the license
ldata=$(cat ./license.txt)

# Apply the license with the following commands

# Log in to the App Node
curl --silent -X POST \
  --cookie "/tmp/cookies.txt" \
  --cookie-jar "/tmp/cookies.txt" \
  -H "Content-Type: application/json" \
  -d '{"username":"'"${admin_username}"'","password":"'"${admin_password}"'"}' \
  ${dr_app_node}/account/login

# Get an API key
api_key=$(curl --silent -X POST \
  --cookie "/tmp/cookies.txt" \
  --cookie-jar "/tmp/cookies.txt" \
  -H "Content-Type: application/json" \
  -d '{"name":"apiKey"}' \
  ${dr_app_node}/api/v2/account/apiKeys/ | cut -d ',' -f 5 | cut -d '"' -f 4)

# Apply the license
curl --silent -w "%{http_code}\n" -X PUT \
  -H "Content-Type: application/json" \
  -H "HTTP/1.1" \
  -H "Authorization: Token ${api_key}}" \
  -d '{"licenseKey":"'"${ldata}"'"}' \
  ${dr_app_node}/api/v2/clusterLicense/

# Expected output: 200
```
---

title: Use DataRobot Prime with AWS Lambda
description: Use DataRobot Prime to download a DataRobot model and deploy it using AWS Lambda.
---

# Use DataRobot Prime models with AWS Lambda {: #use-datarobot-prime-models-with-aws-lambda }

!!! info "Availability information"
    The ability to create _new_ DataRobot Prime models has been removed from the application. This does not affect _existing_ Prime models or deployments. To export Python code in the future, use the Python Scoring Code export function in any RuleFit model.


This page outlines how you can use the DataRobot Prime model functionality to download a DataRobot model and deploy it using AWS Lambda.

DataRobot Prime can be initiated on most DataRobot models and allows you to download an approximation of a DataRobot model either as a Python or Java module.
The code is then easily deployable in any environment and is not dependent on the DataRobot application.
Before proceeding with this workflow, be sure to download a DataRobot Prime model as a Java module.

## Why deploy on AWS Lambda {: #why-deploy-on-aws-lambda }

While DataRobot provides its own scalable prediction servers that are fully integrated with the platform, there are multiple reasons why you would want to deploy on AWS Lambda:

* Company policy or governance decision.

* Serverless architecture.

* Cost reduction.

* Custom functionality on top of the DataRobot model.

* The ability to integrate models into systems that cannot communicate with the DataRobot API.

## Setup {: #setup }

Follow the steps below to complete setup for this example.

1. Rename the Java file to `Prediction.java`.

2. Create a project, “lambda_prime_example”, in the IDE of your choice.

3. Copy `Prediction.java` to the project.

    ![](images/int_udpmwal_large.png)

4. Compile the package using the `mvn package` command.

5. Click **Create function** to create a new AWS function.

    ![](images/int_udpmwal_large_005.png)

6. Choose Java 11 to create Lambda.

    ![](images/int_udpmwal_large_008.png)

7. Give the function a name and choose the permissions.

    ![](images/int_udpmwal_large_007.png)

8. Upload the compiled JAR file to Lambda.

    ![](images/int_udpmwal_large_006.png)

9. Change the Lambda handler to the name of the Java package method:

    ```
    com.datarobot.examples.scoring.prime.PrimeLambdaExample::handleRequest
    ```

Setup is now complete. Next, perform testing to verify that the deployment is working as intended.

## Test the Lambda function {: #test-the-lambda-function }

To test the Lambda function:


1. Go to the TEST event configuration page.

    ![](images/int_udpmwal_large_002.png)

2. Put JSON with features as test event:

    ![](images/int_udpmwal_large_003.png)

3. Click the **Test** button.

    ![](images/int_udpmwal_large_004.png)

Now, you can configure the integration with an AWS API Gateway or other services so that data is sent to the Lambda function and you receive results back.
---
title: Pages
description: Use pages in No-Code AI Apps to organize and group insights.

---

# Pages {: #pages }

Pages divide an application into separate sections that you can navigate between&mdash;allowing you to organize and group insights in a way that makes sense for your use case. By default, each non-time series application has the following pages:

| Page       | Description |
| ---------- | ----------- |
| Home      |  View the application landing page, where you can upload batch predictions and view individual prediction rows.  |
| Create Prediction / Create Optimization      | Make single record predictions (non-time series only).  |
| Prediction Details      | View prediction results for individual prediction rows.  |

To manage your pages, click the **Pages** panel icon on the left or open the **Editing page** dropdown and click **Manage pages**. The **Editing page** dropdown is also how you select the page you want to edit.

![](images/app-build-12.png)

In the **Pages** panel, you can:

|  &nbsp; | Element	| Description |
|---|---|---|
| ![](images/icon-1.png)| + Add	| Adds a new page to the application. |
| ![](images/icon-2.png) |  Reorder  | Modifies the order of the pages. |
| ![](images/icon-3.png) |  Rename | Renames a page.  |
| ![](images/icon-4.png) |  More options	| Deletes or hides a page. |
| ![](images/icon-5.png) |  Editing page	| Controls the application page you are currently editing. |

Pages are displayed at the top of the application.

![](images/app-pages-5.png)

## Create pages {: #create-pages }

In addition to the default pages described above (Home, Create, Prediction Details), you can customize applications by creating pages new pages. You may want to do this, for example, to more intuitively group insights for your specific use case.

To create a new page, open the **Pages** panel and click **+ Add**.

![](images/app-pages-1.png)

You can then click the pencil icon to rename the page (1) and drag-and-drop it to a new position (2).

![](images/app-pages-2.png)

After publishing your changes and leaving Build mode, your new page is displayed along the top of the application.

![](images/app-pages-3.png)

## Delete pages {: #delete-pages }

If you want to remove a page from the end-user application, you can either hide or delete the page. Hiding the page, means it's no longer accessible when using the application, but the page and its contents are preserved. You may want to hide a page, for example, while you're working on it until it's ready to be shared publicly. Deleting a page removes it from the application entirely and it cannot be restored.

To hide or remove a page, open the **Pages** panel and click the more actions icon. Then, select the **Hide** or **Delete**.

![](images/app-pages-4.png)

!!! note
    You cannot delete default pages, however, you can hide them from the end-user application.
---
title: Get started
description: Understand the components of the new DataRobot user interface.

---

# Get started in Workbench {: #get-started-in-workbench }

Workbench is an intuitive, guided, machine learning workflow, providing a way for users to experiment and iterate. Move from raw data to prepared, partitioned data that’s ready for modeling, build many models at once, and generate value quickly through key insights and predictions.

![](images/uxr-2.png)

The following sections help to understand DataRobot's Workbench interface:

Topic   |   Describes...
--------|-----------------
[Workbench overview](wb-overview) | The value and benefits of Workbench, as a generational leap from classic DataRobot, as well as its components and architecture.
[Workbench glossary](wb-glossary) | Terms specific to the Workbench user experience for handy, quick reference.
[Capability matrix](wb-capability-matrix) | An evolving comparison of capabilities available in DataRobot Classic and Workbench.
---
title: Create AI models
description: The tutorials in this section provide quick, task-based instructions for achieving common tasks related to modeling.

---

# Create AI models {: #create-ai-models }

The content in this section provides quick FAQ answers as well as task-based tutorials for achieving common tasks related to modeling.

Topic | Describes how to...
----- | ------
[Select a  target](tut-target) | Select a target feature for your project.
[Set the modeling mode](tut-model-mode) | Choose the appropriate modeling mode for your project.
---
title: Modeling details
description: This section introduces the model building process; data partitioning and validation; features for working with a project after model building completes, including Generate AI report, Export charts and data; and links to details on its component tasks.

---

# Modeling details {: #modeling-details }

This section provides details into components of the functionality that makes up the model building process.

Topic | Describes...
----- | ------
 **Data** |  :~~:  
[Exploratory Data Analysis](eda-explained) | Details of Exploratory Data Analysis (EDA), phases 1  and 2.
[Data partitioning and validation](data-partitioning) | Describes validation types and data partitioning methods.
 **Modeling** |  :~~:  
[Modeling process details](model-ref) | Bits and pieces of the initial model building process.
[Leaderboard reference](leaderboard-ref) | Components of the Leaderboard, blender models, and asterisked scores.
[Model recommendation process](model-rec-process) | Steps involved in DataRobot's selection of a recommended model.
[Sliced insights](sliced-insights) |  View and compare insights based on segments of a project’s data.
[SHAP reference](shap) | Details of SHapley Additive exPlanations, the coalitional game theory framework.
[XEMP calculations](xemp-calc) | Describes the calculations used to determine XEMP qualitative strength.
 **Miscellaneous** |  :~~:  
[Optimization metrics](opt-metric) | Short descriptions of all metrics available for model building.
[Generate AI Report](generate-ai-report) | Create a report of modeling results and insights.
[Export charts and data](export-results) | Download created insights.
[Worker Queue](worker-queue) | Manage models and projects and export data.